Abstract

Touch screens are a key component of consumer mobile devices such as smartphones and

tablets, as well as an increasingly common self-service component of information retrieval on

fixed screens and mobile devices in-store. The ubiquity of touch screens in daily life

increases consumer accessibility and extended use for shopping, whilst software innovations

have increased the functionality of touch screens, for example the extent to which images

respond to fingertip control. This study examines how users engage with interactive visual

rotation and tactile simulation features while browsing fashion clothing products on touch

screen devices and thus contributes to retail touch screen research that previously focused on

in-store kiosks and window displays. Findings show that three dimensions of user

engagement (endurability, novelty and felt involvement) are positively influenced by both

forms of manipulation. In order to examine the extent to which touch screen user engagement

varies with individual preferences for an in-store experience, the paper also examines

whether user engagement outcomes are mediated by an individual's need for physical touch.

Findings show that the need for touch does not explain the variance between individuals. We

conclude that touch screen technology complements the physical retail environment.  

Keywords: touch screen; engagement; smart retail; clothing; haptics; image interactivity.

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1. Introduction

Touch screens have transitioned from being present on consumer mobile devices such as

smartphones and tablets to becoming an increasingly popular self-service technology present

within the retail environment in a range of forms such as information kiosks, window

displays and check-outs (Tüzün, Telli & Alır, 2016). Touch screen technology has a strong

appeal for consumers as it allows them to use their fingertips and removes the need for any

intermediary devices (i.e. a mouse or a stylus) when retrieving information (Benko, Wilson,

& Baudisch, 2006). Survey evidence shows that touch screen presence increases intention to

visit a physical store amongst 65% of UK consumers (Gilmartin, 2016) and thus the in-store

touch screen provides a competitive response to online challengers (RSR, 2016a) by allowing

conmsumers to find out more about products or customise their shopping experience. The

ubiquity of touch screens on both consumer mobile devices as well as being fixed in-store

increases consumer accessibility and enables extended use both in-store and outwith the store

(Gilmartin, 2016; RSR, 2016b; Shankar, Kleijnen, Ramanathan, Rizley, Holland &

Morrissey, 2016). In the UK and US, mobile traffic now constitutes the greatest proportion of

ecommerce traffic (eMarketer, 2016) and online shopping is the most popular web browsing

activity for smartphone users in the UK (Deloitte, 2016).

There is a recognised need for research that examines consumer perceptions of specific

touch screen features on mobile devices (Pantano & Priporas 2016; Blazquez, 2014). For

fashion retailers in particular, ensuring the effectiveness of screen-based product views is a

widely discussed challenge (Kim, Kim & Lennon, 2007; Klatzky & Peck, 2012; Eroglu,

Karen, Machleit & Lenita, 2001) and mobile marketing requires a distinct set of

competencies (Ström, Vendel & Bredican, 2014). Chung’s (2015) experimental study found

use of touch screens to browse an item of clothing led to greater shopper engagement, which 2

subsequently led to higher satisfaction with shopping, higher purchase intentions and more

positive product evaluations. Focusing on psychological ownership, Brasel and Gips (2014)

found that browsing on touch screen devices led to higher product valuations than on PCs,

concluding that consumer perceptions of online products are filtered through the lens of the

interfaces used to explore them.

It is important for retailers to understand the opportunities to enrich the customer shopping

experience on mobile devices and in-store touch screens, as mobile marketing requires a

distinct set of competencies (Ström, Vendel & Bredican, 2014). Touch screens have the

potential to enhance the in-store customer experience by bringing the benefits of online

shopping to ecommerce-savvy consumers (Gilmartin, 2016; RSR, 2016b). Developments in

image interactivity technology (IIT) improve product presentation techniques by enabling

customers to manipulate images in real-time rather than simply viewing static images. For

example, single- and multi-finger gestures such as flicking, rotating and even pinching can be

used to access and interact with product views on touch screens (Padilla, Orzechowski &

Chantler, 2012; Orzechowski, Padilla, Atkinson, Chantler, Baurley, Bianchi-Berthouze,

Watkins & Petreca, 2012). IIT has a positive impact on fulfilling users’ hedonic needs and

positively influences affective aspects of the consumer experience (Teo, Oh, Liu & Wei,

2003; Kim & Forsythe, 2007; Lee, Kim & Fiore, 2010). Consequently, it contributes to

positive attitudes towards the retailer (Wu, 2005). It is important to determine the extent to

which IIT innovation on mobile devices results in user engagement, since the result of failing

to engage consumers could be a lost sale, a disloyal consumer or a failure to transmit

information online (O’Brien & Toms, 2008). In contrast, engaged shoppers are likely to

purchase more items, more frequently than non-engaged shoppers (Shankar, Kleijnen,

Ramanathan, Rizley, Holland & Morrissey, 2016). However, there is a paucity of research 3

that examines consumer perceptions of specific touch screen features on mobile devices

(Blazquez, 2014).

This study contributes to the literature on how differences in IIT influence user

engagement when browsing clothing images. It links emerging touch screen technology to

the stream of research into user engagement commenced by O’Brien and Toms (2008; 2010;

2012). The use of innovative technologies contributes to increasing the value of online retail

(Kim & Forsythe, 2007) and if consumers consider their browsing experience as a success

and feel highly involved in their shopping task, this should lead to an increased intention to

purchase, improvement of the overall online experience, revisit intention or time spent in the

website (Merle, Senecal & St-Onge, 2012; Lee, Kim & Fiore, 2010; Park, Lennon & Stoel,

2005). In this study, data was gathered using a between-subjects design to test the effect of

two sensory stimuli, vision and simulated touch, whilst controlling for the individual trait of

need for touch whilst shopping. The remainder of this paper will explain the conceptual

background, describe the method, report the results, discuss implications for practitioners and

researchers and conclude with limitations and recommendations for further research.

2. Literature Review

2.1 User Engagement

The construct of user engagement combines behavioural, cognitive and affective responses

when using computer-based tools (O’Brien & Toms, 2008; Wiebe, Lamb, Hardy & Sharek,

2014). User engagement occurs progressively from initial “users’ assessment of, and

interaction with, interactive media interfaces, followed by deeper absorption with media

content and behavioral outcomes” (Oh, Bellur & Sundar; 2015, p3). User engagement is

particularly suitable as a way of understanding responses to touch screen technology for the

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acquisition of product information, as it incorporates both hedonic perceptions such as flow

(Trevino & Webster, 1992), utilitarian interface experiences such as perceived usability

(Davis, 1989) and task-technology fit (Goodhue & Thompson; 1995). As such, it provides a

“succinct lens” through which to unify and address several established strands of human-

computer interaction research (Wiebe, Lamb, Hardy & Sharek, 2014, p.124).

O’Brien and Toms (2010) proposed six dimensions of user engagement: (1) Aesthetics,

the visual appearance of the website, (2) Endurability, perceived task-technology fit resulting

in intention recommend to others, (3) Felt Involvement, cognitive immersion in task, (4)

Focussed Attention, flow state that results in temporal and environmental disassociation, (5)

Novelty, pleasurable cognitive stimulation and (6) Perceived Usability, the degree of

cognitive effort and affective frustration experienced during use. The User Engagement scale

was developed by O’Brien and Toms (2008; 2010; 2013) through a survey using the scenario

of general online shopping activity (O’Brien & Toms, 2010) and book purchase in a

laboratory experiment comprising search tasks (O’Brien & Toms, 2013). However, neither

study examined user engagement in the context of consumer acquisition of product

information. The process of evaluation of alternatives is an important stage of the shopping

process (Shankar, Kleijnen, Ramanathan, Rizley, Holland & Morrissey, 2016).

Continued research is needed due to the dynamic and complex nature of user engagement,

the documented fluidity of scale items, and the need to guage whether the measure allows

meaningful comparison between different task contexts (O’Brien & Cairns; 2015) and

different application features. Research is also needed to compare user engagement in

response to different sensory software stimuli. There is scant research that makes finer-

grained comparison to human-computer interaction, with exceptions being Visinescu

Sidorova, Jones and Prybutok (2015) who examined the effect of 3D vs 2D image

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manipulation upon cognitive absorption leading to purchase intention and Xu and Sundar

(2016) who differentiated how interactivity and non-interactive elements within a website

influence cognitive processing of content. To address these gaps, the present study

investigates how different forms of IIT influence user engagementwith fashion clothing

information.

2.2 Image Interactivity Technology and Fashion Retailing

Image interactivity technology (IIT) is a website feature that enables the “creation and

manipulation of product or environment images to simulate (or surpass) actual experience

with the product or environment” (Fiore, Kim & Lee, 2005, p.39). This is particularly

relevant for clothing products, which suffer from sensory impoverishment when retailed

online. There has been sustained development of IIT for the fashion retailing context

resulting in a range of IIT features including the ability to rotate and zoom into product

features, the ability to assemble distinct clothing images into one image through mix and

match technology, and the ability to simulate the appearance of clothing upon a body form

through a virtual fitting room (Lee, Kim & Fiore, 2010; Merle, Senecal & St-Onge, 2012).

Each of these features differs in the range of interactivity and the approximation to physical

vision and touch (Yu, Lee & Damhorst, 2012). A high level of interactivity positively

influences affective aspects of the consumer experience (Lee, Kim & Fiore, 2010).

Advances in IIT enable the artificial recreation of tactile and visual sources of product

information for more intuitive and interactive websites, which, in turn, allow consumers to

digitally interact with products using their fingertips in a more natural manner than using a

keyboard and mouse. It is therefore closer to the way in which shoppers would actually

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interact with the item in a physical context (Padilla, Orzechowski & Chantler, 2012;

Orzechowski, Padilla, Atkinson, Chantler, Baurley, Bianchi-Berthouze, Watkins & Petreca,

2012). Overmars and Poels (2015) examined IIT that simulates stroking gestures in the

context of two textile products (a scarf and a blanket) and showed that use improves product

understanding. They highlighted the need for extending research to other ways in which

people handle fabrics, such as scrunching.

Vision and touch make different contributions to product evaluation (Guest & Spence,

2003; Schifferstein, Otten, Thoolen & Hekkert, 2010). There is also a need to connect these

insights beyond product understanding to include user engagement as an experience that

results from interaction.

2.3 Influence of Vision upon User Engagement

Online images of merchandise are high-involvement elements of a website (Eroglu, Karen,

Machleit & Lenita, 2001). Vision is preferred to judge the macrostructural properties of a

product, such as size and shape (Spence & Gallace, 2008). Visualisation technologies, such

as 360 degree product rotation, support evaluation of the shape, size, flow and movement of

garments, which can lead to more realistic judgments (Schlosser, 2003; Kim & Forsythe,

2007; Yu, Lee & Damhorst, 2012). Park, Lennon and Stoel (2005) found 360 degree rotation

on a website to positively affect consumer mood and reduce perceptions of purchase risk,

leading to increased purchase intent. Compared with static images, IIT also enables users to

form more vivid mental images of products, resulting in more realistic judgments (Schlosser,

2003). We formulate the following research proposition (RP) that IIT allowing 360 degree

rotation will result in a greater positive user engagement response than viewing static

pictures.

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RP1: IIT serving the primary function of vision (visual rotation) has a positive and

significant effect on user engagement.

2.4 Influence of Touch upon User Engagement

Tactile cues shape perception of the microstructural features of a product and material

information such as texture (McCabe & Nowlis, 2003; Guest & Spence, 2003). IIT scrunch

and pinch features simulate tactile interaction to provide an approximation of touch-related

experience attributes such as texture and weight (Overmars & Poels, 2015). Overmars and

Poels (2015) found that simulated stroking gestures increased perceived diagnosticity of

product experience attributes and increased perceptions of control. Based on this we propose:

RP2: IIT serving the primary function of touch (tactile function) has a positive and

significant effect on user engagement.

2.5 The Relative Influence of Touch and Vision upon User Engagement

In-store, shoppers may touch, rub and crumple textiles to estimate qualities such as

softness, stretch or roughness (Orzechowski, 2010). Although there is a gap between

handling a physical textile and the perceived qualities of a textile displayed on a screen

(Gallace & Spence, 2014), tactile simulation in online environments increases cognitive

product evaluation (Daugherty, Li & Biocca, 2008). Since touch is preferred to gather

information offline about material properties, such as texture (McCabe & Nowlis, 2003;

Spence & Gallace, 2008), we propose:

RP3: IIT serving the primary function of touch (tactile function) has a greater impact on

user engagement than IIT serving the primary function of vision (visual rotation).

2.6 The Mediating Influence of the Need for Touch8

The need for touch (NFT) whilst shopping is an individual trait defined as “a preference

for the extraction and utilization of information obtained through the haptic system” (Peck &

Childers, 2003, p.431). Individuals with a high NFT are more likely to access haptic or touch-

based information than those individuals with a lower NFT. Peck and Childers (2003) also

differentiated between those who touch products for the sensory pleasure of the experience,

and the instrumental dimension related to those who touch for a rational purpose. As such,

NFT plays an important role in the consumer’s choice of shopping channel (Citrin, Stem,

Spangenberg & Clark, 2003) so that individuals with a high NFT are unlikely to find online

shopping desirable for products that encourage tactile interaction (Dennis, Jayawardhena &

Papamatthaiou, 2010). Hence, we propose that NFT will mediate the degree of user

engagement that is experienced when using IIT for vision and touch:

RP4 NFT will mediate the relationship between IIT use and user engagement.

3. Methodology

3.1 Design and Participants

A between-subjects experiment with two levels of image interactivity (visual rotation and

simulated tactile scrunch) was conducted to undertand the effects of different IIT features on

user engagement. A total of 218 student subjects participated and were recruited from two

UK universities with an incentive of entry into a prize draw for a £50 online shopping

voucher. A student sample was chosen to reflect trends in online shopping patterns in the UK

which show that younger consumers (16-24 years old) buy more clothes online than in-store

(Mintel, 2014). The achieved sample were predominantly female (87%), had purchased

online in the last six months (89%) and browsed online for fashion every week (77%). These

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characteristics reflect statistics which indicate that more female than male shoppers use

mobile devices when browsing and purchasing clothes online (Mintel, 2014).

3.2 Materials and Procedure

A digital tool called Shoogleit (Padilla & Chantler, 2011) was used to create interactive

product visualisations from pre-recorded video by merging visual and tactile inputs (see

Figure 1). Shoogleit adds user-controlled interactivity to any object on a touch screen by

merging user gestures on mobile devices with animations matching the gesture on a real

object. The garments selected were a ladies chiffon dress and a mens cotton shirt. Pilot tests

amongst 20 participants determined that the garments and fabrics selected were perceived as

having visual and tactile attributes (Overmars & Poels, 2015). To control for model

preference in the visual treatment, the models’ heads were cropped.

We used a two-step procedure with the experimental treatment followed by a self-

completed paper questionnaire. After giving informed consent, participants were randomly

allocated to either the control, vision or touch experimental groups. Female participants were

assigned to the chiffon dress and male participants were assigned to the cotton shirt.

Established principles of experimental research design (Field & Hole, 2002) were followed.

Experiments took place in rooms with no other stimuli that could affect the results. In each

group, each subject was asked to browse the clothing item on an iPad for up to 5 minutes as if

evaluating it for possible purchase and then after browsing, given the paper questionnaire to

complete within 15 minutes.

The control group (n=46) were given static images of the front and back of the garment on

the model to view on an iPad (see Figure 2). The vision group (n=79) used Shoogleit rotate

technology on an iPad. This allowed them to use their fingertips to rotate the model in the

garment through 360 degrees (see Figure 3). The touch group (n=92) used Shoogleit scrunch 10

technology on an iPad, which enabled them to pinch and scrunch a section of the clothing

fabric with their fingertips (see Figure 4). This group was also provided with a laminated

picture of the front and back view of the garment on the model so they could relate the fabric

section to the whole garment.

[Insert Figure 2, 3 and 4]

3.3 Measures

3.3.1 User Engagement

User Engagement was measured through the application of 10 items drawn from the

highest factor loading items of O’Brien and Toms’ (2010) User Engagement scale. Responses

were measured on a 7 point Likert scale (1 = totally disagree to 7 = totally agree). Five of the

six proposed dimensions of user engagement were selected. “Aesthetics” was dropped since

this sub-scale measures response to the visual quality of the shopping website as a whole,

whilst this study focuses upon an isoloated website feature and not the total site. Scale

reliability was acceptable with a Cronbach’s alpha value of .78, which is comfortably over

the suggested α of .7 (De Vellis, 2003) and scores were calculated for an overall measure of

user engagement. The User Engagement scale data was normally distributed.

3.3.2 Need for Touch

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Attractiveness of the models wasmeasured on a 10-point Likert-type scale with threeitems (“The model is

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beautiful/handsome,” “She/Helooks good,” and “This model is attractive”). Similarmeasures have been previously used by Peck and Lo-

13

ken (2004), as well as Bian and Foxall (2013). The threeitems in this scale were averaged to form an index,and Cronbach’s alpha for the

14

items was 0.97. To mea-sure product attitudes a 10-point bipolar scale with fouradjective pairs (bad–good, dislike–like, unpleasant–

15

pleasant, negative–positive) was used. Similar scaleshave been used by, for example, Petty, Cacioppo, and

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Schumann (1983), and Debevec and Romeo (1992).Cronbach’s alpha for the four items in this scale was0.97 and the items were

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averaged to form an indexAttractiveness of the models wasmeasured on a 10-point Likert-type scale with three

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items (“The model is beautiful/handsome,” “She/Helooks good,” and “This model is attractive”). Similarmeasures have been previously

19

used by Peck and Lo-ken (2004), as well as Bian and Foxall (2013). The threeitems in this scale were averaged to form an index,

20

and Cronbach’s alpha for the items was 0.97. To mea-sure product attitudes a 10-point bipolar scale with fouradjective pairs (bad–good,

21

dislike–like, unpleasant–pleasant, negative–positive) was used. Similar scaleshave been used by, for example,

22

Petty, Cacioppo, andSchumann (1983), and Debevec and Romeo (1992).Cronbach’s alpha for the four items in this scale was

23

0.97 and the items were averaged to form an index

NFT was measured on a 7 point likert scale (1 = totally disagree to 7 = totally agree) with

6 items adapted from Peck and Childers’ (2003) scale, which contained three items each

relating to sensory and instrumental dimensions (Lee, Chang & Cheng, 2014). The NFT scale

was reliable with Cronbach’s alpha value of .79. The NFT data showed slight negative

skewness and kurtosis. However, this is unlikely to make a significant difference to the

analysis, and with large samples, the risk of an underestimation of variance is low

(Tabachnick & Fidell, 2013).

4. Results

The data analysis reports the main effects between User Engagement and IIT (scrunch and

rotate) and explores whether NFT acts as a covariate for this relationship.

4.1 Influence of Vision and Touch on User Engagement

Table 1 presents the mean scores for each of the experimental conditions. Columns give

each experimental condition and rows show measures for the dimensions of user engagement

according to O’Brien and Toms (2010). The first column shows the control condition, where 24

participants viewed only static images. The results show the lowest mean average of 1.34 for

novelty, which indicates that participants tended to totally disagree that they felt curiosity or

interest during their browsing experience. There was a tendency to somewhat disagree that

static images provided focused attention, which comprises experiences of flow and task

absorption. In contrast, there was total agreement that the browsing experience using static

images delivered perceived usability and a tendency to somewhat agree that there was felt

endurability and felt involvement. This means that participants tended to agree that they felt

in control and not frustrated or mentally taxed when viewing static images and also felt that

their goals had been successfully achieved and remained involved in the browsing

experience.

The second column shows the average means for the visual rotation condition with the

lowest mean of 3.15 for focused attention, which indicates that participants did not feel high

immersion in the browsing task while using this technology. However, they somewhat agreed

with felt involvement, endurability and perceived the technology as novel compared to the

static pictures. The highest mean of 5.52 is for the perceived usability construct, even higher

than its mean for the tactile simulation condition, which shows the importance of visual

simulation for product judgement.

The third column shows the average mean for the tactile simulation/scrunch condition.

With the exception of perceived usability, this condition comprises the highest average means

for all constructs, demonstrating that scrunch technology focuses attention, generates

involvement and endurability and is perceived as novel.

Table 1: Means for Each User Engagement Dimension

Dimension (a) Control Condition (b) Visual Rotation (c) Tactile SimulationFocussed Attention 2.59 3.15 3.32Felt Involvement 4.26 4.91 5.40

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Endurability 4.50 5.11 5.08Novelty 1.34 4.63 4.95Perceived Usability 5.62 5.56 5.45

A one-way analysis of variance (ANOVA) was used to test difference in user engagement

between participant groups. Results show that there is a significant effect of experimental

condition (static picture, rotation, scrunch) upon user engagement (F (2,112) =8.55, p=.00

(r= .07). Planned contrasts using Tukey’s HSD test shows that there were significantly

different scores between the control condition (static picture) and the treatment groups (rotate

and scrunch), but that there was not a significant difference in user engagement between the

rotate and scrunch condition. Results support RP1 and RP2 in that both rotate and scrunch IIT

have a positive and significant effect on user engagement. However, there is no support for

RP3 as there is no statistically significant difference between the scrunch (touch) and rotate

(visual) conditions.

Table 2: Results of Post Hoc Comparison of IIT use on User Engagement

Dependent Variable (a) Control Condition (b) Visual Rotation (c) Tactile SimulationUser Engagement 4.15* (b, c) 4.71* (a) 4.71* (a)Notes: *p<0.05 Superscript letters a, b, or c indicate those conditions that are significantly different from the mean value.

Differences in types of interaction are more apparent when examining the means for all

dimensions (Table 1), with the highest mean always seen for the scrunch (touch) condition,

followed by rotate (vision). The biggest differences between the two were found in the

“involvement” and “novelty” constructs. Static (control) pictures were always the lowest.

This indicates that providing IIT for consumers when browsing clothing on a touch screen

helps them to feel more engaged, hence there is support for both RP1 and RP2. However, the

difference between rotate and scrunch condition is not statistically significant and thus RP3,

which hypothesized that tactile simulation would have a greater impact upon user

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engagement than visual rotation, is not supported. This may be explained by respondents

wishing to interact with and see with the whole garment rather than just a section of the

fabric. This seems commensurate with the physical experience of shopping for clothes, since

consumers may be more likely to pick up a garment on a hanger and turn it around rather than

feeling the fabric.

4.2 Mediating Influence of Need for Touch

A one-way between-groups ANCOVA (Analysis of Covariance) was conducted to

understand whether NFT during browsing had an effect on respondents’ User Engagement

when using IIT. The Levene’s test of equality of error variances showed that we did not

violate assumptions; our variances were not equal (p= .27). The covariate NFT was not

significantly related to user engagement (F, 2, 214= 0.74, p=.39, r=.06) but there remained a

significant effect of IIT upon user engagement after controlling for NFT (F (2, 214) = 8.26,

p=.00, partial eta squared =.072). Therefore, RP4 was not supported. NFT does not act as a

covariate between IIT and user engagement, which suggests that the IIT succeeds in fulfilling

a tactile function.

5. Discussion

This paper contributes empirical evidence of user engagement with touch screen

interactivity. Results show that visual rotation and tactile simulation features on a touch

screen, specifically an iPad, contribute to an increase in user engagement when compared to a

static image. Our work makes a methodological contribution both in its adaption of the

O’Brien and Toms’ (2010) User Engagement scale but also through showing that a reduced

scale of 10 items is reliable.

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The results confirm that the presence of IIT contributes to a higher level of user

engagement and therefore provides a means of increasing the multisensory enjoyment and

entertainment value of browsing on a touch screen. Even if IIT cannot fully translate the

physical properties of clothing to the touch screen, it nevertheless contributes to improving

the tactile information related to high-involvement components for fashion consumers

(Eroglu, Karen, Machleit & Lenita, 2001) and product features that are related to touch

properties (McCabe & Nowlis, 2003).

This paper makes two theoretical contributions: firstly by connecting emerging touch

screen technology to a relatively new psychological construct and scale and meeting a

research need for investigating user engagement in different contexts; secondly, by presenting

results that show that the individual trait of need for touch does not influence the direct

relationship between the sensory stimuli of vision and simulated touch with user engagement.

This provides support for work indicating that smartphone use satisfies a range of individual

psychological needs, including need for touch (Lee, Chang & Cheng, 2014).

There are practical implications for both online and offline retailers. The devices used in

online shopping have evolved from PC to mobile and as a result, retailers need to redesign

interaction to enhance the user experience (Bilgihan, Kandampully & Zhang, 2016). The

challenge here is related on the one hand to the development of technologies capable of

delivering credible and reliable tactile sensations over distance, and on the other to the use

marketing strategies that somehow allow people to ‘touch before buying’. Our experiment

uses an iPad touch screen device which is readily available on the market for use by retailers.

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We show that Shoogleit IIT provides a way of overcoming the lack of physical experience

for online retailers of fabric and apparel products. We show that IIT enables the consumer to

evaluate the product and so increases user engagement, which is a holistic measure of both

hedonic and instrumental responses to the mobile browsing process, reducing the physical-

digital divide and thus enabling retailers to create engaging shopping websites. Results

suggest that consumer uptake of IIT for browsing fabric and apparel products on touch

screens would be positive, given the greater level of engagement that was experienced in the

two experimental conditions, compared to the control condition where only static pictures

were presented. Since engagement with the technology is one of the main factors influencing

consumers to choose one website or application over others (Calder, Malthouse & Schaedel,

2009), retailers should provide augmented types of IIT to create a more immersive mobile

shopping experience.

Shoogleit IIT provides a way of overcoming the lack of physical experience for online

retailers of fabric products. By adopting such technology, online retailers of fabric and

apparel products could potentially increase the value of product information presented,

engage their consumers more effectively in an active shopping experience, increase the

number of unique and repeat traffic visitors for a site, and ultimately establish an online

competitive advantage. Furthermore, better virtual product experiences could enhance how

consumers learn by saving time and eliminating unnecessary information. By using IIT,

online retailers could begin to establish a virtual product experience that alters the way

consumers interact with products and mimics the physical shopping experience to a greater

degree than seen before.

6. Limitations and Suggestions for Future Research 29

We acknowledge that our research is limited by the fact that participants interacted with

the technology in an experimental setting and therefore the general set-up of the study did not

fully represent the natural context of online browsing and shopping. Future research might be

replicated using field trials either in the home environment over an extended period of time of

in a retail environment where goods might not be physically available due to stock

limitations. Data was gathered using a self-report survey and future research might

supplement this method with eye-tracker data or galvanic response sensors to provide greater

insight into the influence of online stimulation on consumer behaviour.

Overall however, our results provide support for future research that seeks to develop

commercially viable (and realistic) touch screen interfaces that could bring the tactile

attributes of the physical shopping experience to online shopping. The rapid development of

mobile augmented reality suggests that there is potential for retail settings to become smarter

and add customer value (Dacko, 2016). Incremental advances in technology, such as IIT,

contribute to bridging the gap between a static experience and a truly haptic one. To

conclude, this study connects with a growing literature on engagement as a behavior across a

range of devices and applications. The results provide a foundation for additional research

into the role of visual and tactile interactivity within the broader field of user engagement and

immersion.

7. Conclusion and Implications

This study examined how user engagement is related to IIT visual (product rotation) and

touch-related features (fabric scrunch) when browsing fashion apparel. In addition, we tested

the extent to which the need for touch strengthens or weakens any identified relationship.

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Data was collected using a two-step experimental design which tested the differences in user-

engagement between three conditions: static image (the control condition), product rotation

and scrunch. Empirical results provide three insights (1) the presence of IIT results in

increased user engagement compared to the control condition, (2) visual or touch-related

features are equal in their effect (3) a user’s need for touch does not affect their engagement

response to IIT. The implication for retailers is that IIT can create an immersive shopping

experience that overcomes the need for physical touch and thus improves the quality of the

online shopping experience. The implications for researchers of human-computer interaction

are that IIT satisfies the sensory need for touch and thus there is a scope to revisit prior

research into the influence of individual psychological traits upon technology response.

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Figures

Figure 1: Shoogleit multi-gesture interface on touch screen (Orzechowski, Padilla,

Atkinson, Chantler, Baurley, Bianchi-Berthouze, Watkins & Petreca, 2012)

Figure 2: Ladies chiffon dress and mens shirt static image

Figure 3: Ladies chiffon dress and mens shirt visual rotation

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Figure 4: Chiffon fabric and shirt fabric “scrunch” tactile simulation

40